Rolling bearing fault diagnosis method based on dual-tree complex wavelet pack manifold domain noise reduction

Abstract

The invention relates to a rolling bearing fault diagnosis method based on dual-tree complex wavelet pack manifold domain noise reduction. The rolling bearing fault diagnosis method based on the dual-tree complex wavelet pack manifold domain noise reduction comprises steps of using an accelerated speed sensor to collect a vibration signal of the rolling bearing, performing dual-tree complex wavelet pack decomposition on the vibration signal, maintaining wavelet pack coefficients of first two nodes, performing threshold noise reduction on wavelet coefficients of the rest nodes, performing single branch reconstruction on the wavelet pack coefficient of each node to perform a high dimensional signal space, using a t distribution random neighbor embedding method to extract low a dimensional manifold, performing inverse reconstruction on the low-dimensional manifold to obtain a high-dimensional space main manifold, obtaining a signal after noise reduction, performing Hilbert envelope demodulation on the signal after noise reduction to obtain an envelope frequency spectrum of the vibration signal, and realizing fault diagnosis of the rolling bearing according to an inner ring fault characteristic frequency and an outer ring fault characteristic frequency of the rolling bearing, a rolling body fault characteristic frequency and a retainer fault characteristic frequency.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a rolling bearing fault diagnosis method based on dual-tree complex wavelet packet manifold domain noise reduction. Background technique [0002] Rotating machinery is widely used in industrial and military fields. Rolling bearings are the core components of rotating machinery, and their performance directly affects the reliability of mechanical equipment. Due to long-term operation, rolling bearings are prone to damage such as cracks, rolling body wear, and cage fracture, which lead to potential safety hazards in mechanical equipment. Therefore, timely and accurate fault diagnosis of rolling bearings is of great significance to improve the stable operation ability of mechanical equipment. [0003] The fault diagnosis of rolling bearings usually adopts a method based on vibration signals, and the fault type of the bearing is judged by extracting the fault char...

AI Technical Summary

Problems solved by technology

The noise reduction method based on wavelet and wavelet packet shrinkage threshold is a widely used signal denoising method, but the traditional wavelet and wavelet packet methods have the following defects: frequency aliasing of wavelet and wavelet packet decomposition, lack of translation invariance and Difficult choice of wavelet basis

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